The goal of this project is to use computer analysis to classify small lung nodules, identified on CT, into likely benign
and likely malignant categories. We compared discrete wavelet transforms (DWT) based features and a modification of
classical features used and reported by others. To determine the best combination of features for classification, several
intensities of white noise were added to the original images to determine the effect of such noise on classification
accuracy. Two different approaches were used to determine the effect of noise: in the first method the best features for
classification of nodules on the original image were retained as noise was added. In the second approach, we
recalculated the results to reselect the best classification features for each particular level of added noise. The CT images
are from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI). For this study, nodules were
extracted in window frames of three sizes. Malignant nodules were cytologically or histogically diagnosed, while benign
had two-year follow-up. A linear discriminant analysis with Fisher criterion (FLDA) approach was used for feature
selection and classification, and decision matrix for matched sample to compare the classification accuracy. The initial
features mode revealed sensitivity to both the amount of noise and the size of window frame. The recalculated feature
mode proved more robust to noise with no change in terms of classification accuracy. This indicates that the best
features for computer classification of lung nodules will differ with noise, and, therefore, with exposure.